{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 2
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython2",
      "version": "2.7.6"
    },
    "colab": {
      "name": "train_shape.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "GI9KZ3F8TLSK"
      },
      "source": [
        "# EfficientDet Training On A Custom Dataset\n",
        "\n",
        "\n",
        "\n",
        "<table align=\"left\"><td>\n",
        "  <a target=\"_blank\"  href=\"https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/blob/master/tutorial/train_logo.ipynb\">\n",
        "    <img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on github\n",
        "  </a>\n",
        "</td><td>\n",
        "  <a target=\"_blank\"  href=\"https://colab.research.google.com/github/zylo117/Yet-Another-EfficientDet-Pytorch/blob/master/tutorial/train_logo.ipynb\">\n",
        "    <img width=32px src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "</td></table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "67-3S5_VTLSL"
      },
      "source": [
        "## This tutorial will show you how to train a custom dataset.\n",
        "\n",
        "## Please enable GPU support to accelerate on notebook setting if you are using colab.\n",
        "\n",
        "### 0. Install Requirements"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "90laRz20TLSN"
      },
      "source": [
        "!pip install pycocotools numpy==1.16.0 opencv-python tqdm tensorboard tensorboardX pyyaml webcolors matplotlib"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "-R5C4DaETLSS"
      },
      "source": [
        "### 1. Prepare Custom Dataset/Pretrained Weights (Skip this part if you already have datasets and weights of your own)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "JmCQj3rhTLSS"
      },
      "source": [
        "import os\n",
        "import sys\n",
        "if \"projects\" not in os.getcwd():\n",
        "  !git clone --depth 1 https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch\n",
        "  os.chdir('Yet-Another-EfficientDet-Pytorch')\n",
        "  sys.path.append('.')\n",
        "else:\n",
        "  !git pull\n",
        "\n",
        "# download and unzip dataset\n",
        "! mkdir datasets\n",
        "! wget https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/releases/download/1.1/dataset_logo.zip\n",
        "! unzip -d datasets/ dataset_logo.zip\n",
        "\n",
        "# download pretrained weights\n",
        "! mkdir weights\n",
        "! wget https://github.com/zylo117/Yet-Another-EfficientDet-Pytorch/releases/download/1.0/efficientdet-d0.pth -O weights/efficientdet-d0.pth\n",
        "\n",
        "# prepare project file projects/logo.yml\n",
        "# showing its contents here\n",
        "! cat projects/logo.yml"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "7Q2onXNZTLSV"
      },
      "source": [
        "### 2. Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "a-eznEu5TLSW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bba632e4-a7f0-4209-a10a-f801ca6a206b"
      },
      "source": [
        "# consider this is a simple dataset, train head will be enough.\n",
        "! python train.py -c 0 -p logo --head_only True --lr 5e-3 --batch_size 32 --load_weights weights/efficientdet-d0.pth  --num_epochs 10 --save_interval 100\n",
        "\n",
        "# the loss will be high at first\n",
        "# don't panic, be patient,\n",
        "# just wait for a little bit longer"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "[Warning] Ignoring Error(s) in loading state_dict for EfficientDetBackbone:\n",
            "\tsize mismatch for classifier.header.pointwise_conv.conv.weight: copying a param with shape torch.Size([810, 64, 1, 1]) from checkpoint, the shape in current model is torch.Size([90, 64, 1, 1]).\n",
            "\tsize mismatch for classifier.header.pointwise_conv.conv.bias: copying a param with shape torch.Size([810]) from checkpoint, the shape in current model is torch.Size([90]).\n",
            "[Warning] Don't panic if you see this, this might be because you load a pretrained weights with different number of classes. The rest of the weights should be loaded already.\n",
            "[Info] loaded weights: efficientdet-d0.pth, resuming checkpoint from step: 0\n",
            "[Info] freezed backbone\n",
            "Step: 17. Epoch: 0/10. Iteration: 18/18. Cls loss: 10.13626. Reg loss: 3.01734. Total loss: 13.15360: 100% 18/18 [00:22<00:00,  1.28s/it]\n",
            "Val. Epoch: 0/10. Classification loss: 2.83116. Regression loss: 3.96368. Total loss: 6.79484\n",
            "Step: 35. Epoch: 1/10. Iteration: 18/18. Cls loss: 1.62736. Reg loss: 3.02261. Total loss: 4.64997: 100% 18/18 [00:22<00:00,  1.27s/it]\n",
            "Val. Epoch: 1/10. Classification loss: 2.02191. Regression loss: 3.54868. Total loss: 5.57059\n",
            "Step: 53. Epoch: 2/10. Iteration: 18/18. Cls loss: 1.32619. Reg loss: 3.12491. Total loss: 4.45111: 100% 18/18 [00:22<00:00,  1.23s/it]\n",
            "Val. Epoch: 2/10. Classification loss: 1.88035. Regression loss: 3.62410. Total loss: 5.50445\n",
            "Step: 71. Epoch: 3/10. Iteration: 18/18. Cls loss: 1.43230. Reg loss: 2.56683. Total loss: 3.99912: 100% 18/18 [00:22<00:00,  1.24s/it]\n",
            "Val. Epoch: 3/10. Classification loss: 1.74555. Regression loss: 3.48976. Total loss: 5.23531\n",
            "Step: 89. Epoch: 4/10. Iteration: 18/18. Cls loss: 1.20437. Reg loss: 2.63474. Total loss: 3.83911: 100% 18/18 [00:22<00:00,  1.24s/it]\n",
            "Val. Epoch: 4/10. Classification loss: 1.64060. Regression loss: 3.38164. Total loss: 5.02225\n",
            "Step: 99. Epoch: 5/10. Iteration: 10/18. Cls loss: 1.18498. Reg loss: 2.47132. Total loss: 3.65630:  50% 9/18 [00:16<00:10,  1.19s/it]checkpoint...\n",
            "Step: 107. Epoch: 5/10. Iteration: 18/18. Cls loss: 1.36353. Reg loss: 2.14976. Total loss: 3.51329: 100% 18/18 [00:22<00:00,  1.23s/it]\n",
            "Val. Epoch: 5/10. Classification loss: 1.55643. Regression loss: 3.18712. Total loss: 4.74355\n",
            "Step: 125. Epoch: 6/10. Iteration: 18/18. Cls loss: 1.30181. Reg loss: 2.35976. Total loss: 3.66157: 100% 18/18 [00:22<00:00,  1.24s/it]\n",
            "Val. Epoch: 6/10. Classification loss: 1.48681. Regression loss: 3.05408. Total loss: 4.54089\n",
            "Step: 143. Epoch: 7/10. Iteration: 18/18. Cls loss: 1.31948. Reg loss: 2.63024. Total loss: 3.94972: 100% 18/18 [00:22<00:00,  1.24s/it]\n",
            "Val. Epoch: 7/10. Classification loss: 1.43246. Regression loss: 2.71716. Total loss: 4.14963\n",
            "Step: 161. Epoch: 8/10. Iteration: 18/18. Cls loss: 1.21941. Reg loss: 2.22280. Total loss: 3.44220: 100% 18/18 [00:22<00:00,  1.24s/it]\n",
            "Val. Epoch: 8/10. Classification loss: 1.38853. Regression loss: 2.77246. Total loss: 4.16099\n",
            "Step: 179. Epoch: 9/10. Iteration: 18/18. Cls loss: 1.29063. Reg loss: 2.84046. Total loss: 4.13109: 100% 18/18 [00:22<00:00,  1.25s/it]\n",
            "Val. Epoch: 9/10. Classification loss: 1.35342. Regression loss: 2.76418. Total loss: 4.11760\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U09nvLg_ss1v",
        "outputId": "0226898e-58bd-4420-818f-2e6d0ea203c4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "source": [
        "! python train.py -c 0 -p logo --head_only False --lr 1e-3 --batch_size 16 --load_weights last  --num_epochs 30 --save_interval 100"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "using weights logs//logo/efficientdet-d0_9_180.pth\n",
            "[Info] loaded weights: efficientdet-d0_9_180.pth, resuming checkpoint from step: 180\n",
            "Step: 184. Epoch: 4/30. Iteration: 37/37. Cls loss: 1.01281. Reg loss: 2.37766. Total loss: 3.39047: 100% 37/37 [00:16<00:00,  2.27it/s]\n",
            "Val. Epoch: 4/30. Classification loss: 1.00430. Regression loss: 2.72571. Total loss: 3.73001\n",
            "Step: 199. Epoch: 5/30. Iteration: 15/37. Cls loss: 0.75802. Reg loss: 1.94845. Total loss: 2.70647:  38% 14/37 [00:19<00:20,  1.14it/s]checkpoint...\n",
            "Step: 221. Epoch: 5/30. Iteration: 37/37. Cls loss: 0.61092. Reg loss: 1.65059. Total loss: 2.26151: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 5/30. Classification loss: 1.00675. Regression loss: 2.32956. Total loss: 3.33631\n",
            "Step: 258. Epoch: 6/30. Iteration: 37/37. Cls loss: 0.45622. Reg loss: 0.91580. Total loss: 1.37202: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 6/30. Classification loss: 0.98546. Regression loss: 2.01005. Total loss: 2.99551\n",
            "Step: 295. Epoch: 7/30. Iteration: 37/37. Cls loss: 0.45474. Reg loss: 0.66668. Total loss: 1.12142: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 7/30. Classification loss: 0.62457. Regression loss: 1.70118. Total loss: 2.32575\n",
            "Step: 299. Epoch: 8/30. Iteration: 4/37. Cls loss: 0.39393. Reg loss: 1.04028. Total loss: 1.43421:   8% 3/37 [00:10<02:25,  4.27s/it]checkpoint...\n",
            "Step: 332. Epoch: 8/30. Iteration: 37/37. Cls loss: 0.45709. Reg loss: 0.73528. Total loss: 1.19237: 100% 37/37 [00:37<00:00,  1.00s/it]\n",
            "Val. Epoch: 8/30. Classification loss: 0.60049. Regression loss: 1.83719. Total loss: 2.43768\n",
            "Step: 369. Epoch: 9/30. Iteration: 37/37. Cls loss: 0.35229. Reg loss: 0.77125. Total loss: 1.12355: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 9/30. Classification loss: 0.47868. Regression loss: 1.54078. Total loss: 2.01946\n",
            "Step: 399. Epoch: 10/30. Iteration: 30/37. Cls loss: 0.36470. Reg loss: 0.48757. Total loss: 0.85227:  78% 29/37 [00:31<00:06,  1.29it/s]checkpoint...\n",
            "Step: 406. Epoch: 10/30. Iteration: 37/37. Cls loss: 0.33900. Reg loss: 0.99633. Total loss: 1.33533: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 10/30. Classification loss: 0.44765. Regression loss: 1.48790. Total loss: 1.93555\n",
            "Step: 443. Epoch: 11/30. Iteration: 37/37. Cls loss: 0.33779. Reg loss: 0.64525. Total loss: 0.98304: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 11/30. Classification loss: 0.42687. Regression loss: 1.46713. Total loss: 1.89400\n",
            "Step: 480. Epoch: 12/30. Iteration: 37/37. Cls loss: 0.22535. Reg loss: 0.39621. Total loss: 0.62156: 100% 37/37 [00:37<00:00,  1.00s/it]\n",
            "Val. Epoch: 12/30. Classification loss: 0.43481. Regression loss: 1.52932. Total loss: 1.96413\n",
            "Step: 499. Epoch: 13/30. Iteration: 19/37. Cls loss: 0.21911. Reg loss: 0.46588. Total loss: 0.68499:  49% 18/37 [00:22<00:15,  1.26it/s]checkpoint...\n",
            "Step: 517. Epoch: 13/30. Iteration: 37/37. Cls loss: 0.19952. Reg loss: 0.35744. Total loss: 0.55696: 100% 37/37 [00:37<00:00,  1.00s/it]\n",
            "Val. Epoch: 13/30. Classification loss: 0.39304. Regression loss: 1.44413. Total loss: 1.83717\n",
            "Step: 554. Epoch: 14/30. Iteration: 37/37. Cls loss: 0.19985. Reg loss: 0.27957. Total loss: 0.47941: 100% 37/37 [00:37<00:00,  1.00s/it]\n",
            "Val. Epoch: 14/30. Classification loss: 0.37317. Regression loss: 1.35620. Total loss: 1.72936\n",
            "Step: 591. Epoch: 15/30. Iteration: 37/37. Cls loss: 0.21125. Reg loss: 0.36408. Total loss: 0.57533: 100% 37/37 [00:37<00:00,  1.00s/it]\n",
            "Val. Epoch: 15/30. Classification loss: 0.35804. Regression loss: 1.25017. Total loss: 1.60821\n",
            "Step: 599. Epoch: 16/30. Iteration: 8/37. Cls loss: 0.18205. Reg loss: 0.50824. Total loss: 0.69028:  19% 7/37 [00:14<00:48,  1.62s/it]checkpoint...\n",
            "Step: 628. Epoch: 16/30. Iteration: 37/37. Cls loss: 0.18887. Reg loss: 0.35197. Total loss: 0.54084: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 16/30. Classification loss: 0.39401. Regression loss: 1.37691. Total loss: 1.77092\n",
            "Step: 665. Epoch: 17/30. Iteration: 37/37. Cls loss: 0.21641. Reg loss: 0.32194. Total loss: 0.53836: 100% 37/37 [00:37<00:00,  1.00s/it]\n",
            "Val. Epoch: 17/30. Classification loss: 0.32397. Regression loss: 1.34240. Total loss: 1.66638\n",
            "Step: 699. Epoch: 18/30. Iteration: 34/37. Cls loss: 0.15946. Reg loss: 0.27132. Total loss: 0.43079:  89% 33/37 [00:34<00:03,  1.29it/s]checkpoint...\n",
            "Step: 702. Epoch: 18/30. Iteration: 37/37. Cls loss: 0.14708. Reg loss: 0.24192. Total loss: 0.38900: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 18/30. Classification loss: 0.31525. Regression loss: 1.21253. Total loss: 1.52778\n",
            "Step: 739. Epoch: 19/30. Iteration: 37/37. Cls loss: 0.11557. Reg loss: 0.25368. Total loss: 0.36925: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 19/30. Classification loss: 0.35022. Regression loss: 1.26635. Total loss: 1.61657\n",
            "Step: 776. Epoch: 20/30. Iteration: 37/37. Cls loss: 0.13499. Reg loss: 0.33121. Total loss: 0.46620: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 20/30. Classification loss: 0.38364. Regression loss: 1.33795. Total loss: 1.72158\n",
            "Step: 799. Epoch: 21/30. Iteration: 23/37. Cls loss: 0.13240. Reg loss: 0.19544. Total loss: 0.32785:  59% 22/37 [00:26<00:11,  1.26it/s]checkpoint...\n",
            "Step: 813. Epoch: 21/30. Iteration: 37/37. Cls loss: 0.09243. Reg loss: 0.19516. Total loss: 0.28759: 100% 37/37 [00:37<00:00,  1.02s/it]\n",
            "Val. Epoch: 21/30. Classification loss: 0.31758. Regression loss: 1.28901. Total loss: 1.60659\n",
            "Step: 850. Epoch: 22/30. Iteration: 37/37. Cls loss: 0.08277. Reg loss: 0.25631. Total loss: 0.33909: 100% 37/37 [00:37<00:00,  1.02s/it]\n",
            "Val. Epoch: 22/30. Classification loss: 0.35107. Regression loss: 1.20895. Total loss: 1.56002\n",
            "Step: 887. Epoch: 23/30. Iteration: 37/37. Cls loss: 0.08827. Reg loss: 0.32577. Total loss: 0.41404: 100% 37/37 [00:37<00:00,  1.00s/it]\n",
            "Val. Epoch: 23/30. Classification loss: 0.33417. Regression loss: 1.20227. Total loss: 1.53644\n",
            "Step: 899. Epoch: 24/30. Iteration: 12/37. Cls loss: 0.07027. Reg loss: 0.23861. Total loss: 0.30888:  30% 11/37 [00:17<00:26,  1.03s/it]checkpoint...\n",
            "Step: 924. Epoch: 24/30. Iteration: 37/37. Cls loss: 0.10675. Reg loss: 0.25002. Total loss: 0.35677: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 24/30. Classification loss: 0.33806. Regression loss: 1.17233. Total loss: 1.51038\n",
            "Step: 961. Epoch: 25/30. Iteration: 37/37. Cls loss: 0.06641. Reg loss: 0.23409. Total loss: 0.30050: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 25/30. Classification loss: 0.32498. Regression loss: 1.23174. Total loss: 1.55672\n",
            "Step: 998. Epoch: 26/30. Iteration: 37/37. Cls loss: 0.05673. Reg loss: 0.23390. Total loss: 0.29064: 100% 37/37 [00:37<00:00,  1.00s/it]\n",
            "Val. Epoch: 26/30. Classification loss: 0.33367. Regression loss: 1.19999. Total loss: 1.53365\n",
            "Step: 999. Epoch: 27/30. Iteration: 1/37. Cls loss: 0.05013. Reg loss: 0.17221. Total loss: 0.22233:   0% 0/37 [00:08<?, ?it/s]checkpoint...\n",
            "Step: 1035. Epoch: 27/30. Iteration: 37/37. Cls loss: 0.04380. Reg loss: 0.16828. Total loss: 0.21209: 100% 37/37 [00:37<00:00,  1.02s/it]\n",
            "Val. Epoch: 27/30. Classification loss: 0.34937. Regression loss: 1.23404. Total loss: 1.58341\n",
            "Step: 1072. Epoch: 28/30. Iteration: 37/37. Cls loss: 0.07173. Reg loss: 0.14566. Total loss: 0.21739: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 28/30. Classification loss: 0.33761. Regression loss: 1.17833. Total loss: 1.51595\n",
            "Step: 1099. Epoch: 29/30. Iteration: 27/37. Cls loss: 0.04282. Reg loss: 0.18612. Total loss: 0.22894:  70% 26/37 [00:29<00:08,  1.29it/s]checkpoint...\n",
            "Step: 1109. Epoch: 29/30. Iteration: 37/37. Cls loss: 0.05454. Reg loss: 0.19500. Total loss: 0.24953: 100% 37/37 [00:37<00:00,  1.01s/it]\n",
            "Val. Epoch: 29/30. Classification loss: 0.36328. Regression loss: 1.14007. Total loss: 1.50335\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "id": "05mjrGRETLSZ"
      },
      "source": [
        "### 3. Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "9yzNyaSxTLSZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "642e7fca-e729-40b0-82a7-aa466820bd9e"
      },
      "source": [
        "#get latest weight file\n",
        "%cd logs/logo\n",
        "weight_file = !ls -Art | grep efficientdet\n",
        "%cd ../..\n",
        "\n",
        "#uncomment the next line to specify a weight file\n",
        "#weight_file[-1] = 'efficientdet-d0_49_1400.pth'\n",
        "\n",
        "! python coco_eval.py -c 0 -p logo -w \"logs/logo/{weight_file[-1]}\""
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/Yet-Another-EfficientDet-Pytorch/logs/logo\n",
            "/content/Yet-Another-EfficientDet-Pytorch\n",
            "running coco-style evaluation on project logo, weights logs/logo/efficientdet-d0_29_1110.pth...\n",
            "loading annotations into memory...\n",
            "Done (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "100% 100/100 [00:06<00:00, 15.87it/s]\n",
            "Loading and preparing results...\n",
            "DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "BBox\n",
            "Running per image evaluation...\n",
            "Evaluate annotation type *bbox*\n",
            "DONE (t=0.08s).\n",
            "Accumulating evaluation results...\n",
            "DONE (t=0.07s).\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.525\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.702\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.602\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.562\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.507\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.482\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.566\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.603\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.603\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.621\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.586\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.551\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": false,
        "pycharm": {
          "name": "#%% md\n"
        },
        "id": "zhV3bNF3TLSc"
      },
      "source": [
        "### 4. Visualize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "pycharm": {
          "name": "#%%\n"
        },
        "id": "uEDHMAIJTLSc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "a9b31282-e4eb-444b-bb83-a39cb8d99574"
      },
      "source": [
        "import torch\n",
        "from torch.backends import cudnn\n",
        "\n",
        "from backbone import EfficientDetBackbone\n",
        "import cv2\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "from efficientdet.utils import BBoxTransform, ClipBoxes\n",
        "from utils.utils import preprocess, invert_affine, postprocess\n",
        "\n",
        "compound_coef = 0\n",
        "force_input_size = None  # set None to use default size\n",
        "img_path = 'datasets/logo/val/208.jpg'\n",
        "\n",
        "threshold = 0.2\n",
        "iou_threshold = 0.2\n",
        "\n",
        "use_cuda = True\n",
        "use_float16 = False\n",
        "cudnn.fastest = True\n",
        "cudnn.benchmark = True\n",
        "\n",
        "obj_list = [ 'adidas0', 'chanel','gucci','hh','lacoste','mk','nike','prada','puma','supreme' ]\n",
        "\n",
        "# tf bilinear interpolation is different from any other's, just make do\n",
        "input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536]\n",
        "input_size = input_sizes[compound_coef] if force_input_size is None else force_input_size\n",
        "ori_imgs, framed_imgs, framed_metas = preprocess(img_path, max_size=input_size)\n",
        "\n",
        "if use_cuda:\n",
        "    x = torch.stack([torch.from_numpy(fi).cuda() for fi in framed_imgs], 0)\n",
        "else:\n",
        "    x = torch.stack([torch.from_numpy(fi) for fi in framed_imgs], 0)\n",
        "\n",
        "x = x.to(torch.float32 if not use_float16 else torch.float16).permute(0, 3, 1, 2)\n",
        "\n",
        "model = EfficientDetBackbone(compound_coef=compound_coef, num_classes=len(obj_list),\n",
        "\n",
        "                             # replace this part with your project's anchor config\n",
        "                             ratios=[(1.0, 1.0), (1.3, 0.8), (1.9, 0.5)],\n",
        "                             scales=[2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)])\n",
        "\n",
        "model.load_state_dict(torch.load('logs/logo/'+weight_file[-1]))\n",
        "model.requires_grad_(False)\n",
        "model.eval()\n",
        "\n",
        "if use_cuda:\n",
        "    model = model.cuda()\n",
        "if use_float16:\n",
        "    model = model.half()\n",
        "\n",
        "with torch.no_grad():\n",
        "    features, regression, classification, anchors = model(x)\n",
        "\n",
        "    regressBoxes = BBoxTransform()\n",
        "    clipBoxes = ClipBoxes()\n",
        "\n",
        "    out = postprocess(x,\n",
        "                      anchors, regression, classification,\n",
        "                      regressBoxes, clipBoxes,\n",
        "                      threshold, iou_threshold)\n",
        "\n",
        "out = invert_affine(framed_metas, out)\n",
        "\n",
        "for i in range(len(ori_imgs)):\n",
        "    if len(out[i]['rois']) == 0:\n",
        "        continue\n",
        "    ori_imgs[i] = ori_imgs[i].copy()\n",
        "    for j in range(len(out[i]['rois'])):\n",
        "        (x1, y1, x2, y2) = out[i]['rois'][j].astype(np.int)\n",
        "        cv2.rectangle(ori_imgs[i], (x1, y1), (x2, y2), (255, 255, 0), 2)\n",
        "        obj = obj_list[out[i]['class_ids'][j]]\n",
        "        score = float(out[i]['scores'][j])\n",
        "\n",
        "        cv2.putText(ori_imgs[i], '{}, {:.3f}'.format(obj, score),\n",
        "                    (x1, y1 + 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5,\n",
        "                    (255, 255, 0), 1)\n",
        "\n",
        "        plt.imshow(ori_imgs[i])\n",
        "\n"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}